; Population density profiles of nasopharyngeal carriage of five bacterial species in pre-school children measured using quantitative PCR offer potential insights into the dynamics of transmission. Hum Vaccin Immunother. 2015 Sep 14:0.

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Recently announced, and not yet up for sale, DJI has an intriguing new drone on their hands. The Matrice line of commercial and professional grade drones has long been the workhorse of DJI’s quad and multi-rotor drones, leaving many niceties to the
retail drones like the Phantom and Inspire 2
. Imagine combining the very best that the Inspire 2 has to offer into a heavy-duty configurable machine, that’s the
M200 series
.

All the best flight modes and safety features, heavy lifting with multiple camera options and configurations. We’re talking about mounting a camera on top to face upward, for things like bridge inspection. We’re also talking about hanging two cameras on the bottom, to shoot infrared and high-zoom at the same time. Sound hard to operate, no worries, one pilot can control the cameras while the second controls the drone through the dedicated FPV camera, just like the Inspire 2.

This may be priced out of range for most of us, but as far as commercial use goes, this drone is as sleek, powerful and feature rich as we’ve seen yet.

We are falling in love with drones, and we hope you are too. From the starter
toy drones
all the way up to
multi-thousand dollar rigs
, we will keep you in the loop, check back for updates to this list as new and exciting drones hit the market.

Price history for our favorite drones:

We are asking a lot of you to just trust us when we offer our recommendations on
drones
. Allow us to briefly talk about what we’re doing here today. Not just today, actually, we employ the same techniques and philosophy for all of our content on the site. We want you to get the most of your flying experience, which includes safety, value for your dollar and accurately identifying the capabilities of each drone.

Hey guys, this is Jonathan Feist. As a little history, I have been an enthusiast of flight for many years. My youthful enthusiasm matured into something real when I began studying government issued Aviation Training material in 2007, eager to get my private pilot’s license, or better yet, to become a helicopter pilot. I flew my first real life airplane in 2009, the little Cessna you see above. (Thank you again, Ethan!) Sadly, that was also my last time at the helm of a passenger aircraft, for now.

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FIG. 2.

Effect of 50 mM coaggregation inhibitors on autoaggregation. cells were incubated in SDM containing 25% saliva and several different coaggregation inhibitors. The samples from left to right are as follows: water, lactose, galactose, arginine, and lysine. The results presented here are the averages of three independent samples. This experiment was performed two times, with similar results each time.

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FIG. 3.

Effect of lysine concentration on autoaggregation. cells were incubated in SDM containing a range of lysine concentrations and measured for OD every 10 min. The lysine concentrations are as follows: squares, 0 mM; triangles, 6.25 mM; asterisks, 12.5 mM; circles, 25 mM; and crosses, 50 mM. The results presented here are the averages of three independent samples. This experiment was performed three times, with similar results each time.

Transcriptome analysis of planktonic versus aggregated cells.
Given the strong propensity for 25586 to aggregate under the conditions of our assay, our next goal was to determine whether aggregation would elicit any gene responses from the cells. For this purpose, we decided to take a microarray approach to measure the effect of intraspecies cell aggregation upon the transcriptome. Since saliva could induce aggregation and lysine could inhibit it, we could take two potential microarray approaches (Fig.
4
). We could either measure the transcriptome of cells incubated in (condition 1) SDM plus saliva (aggregation) versus SDM (planktonic) or (condition 2) SDM plus saliva (aggregation) versus SDM plus saliva plus lysine (planktonic). However, both approaches had the potential drawback of measuring gene responses to aggregation, as well as differences in medium composition (i.e., saliva for the first approach or lysine in the second approach). In order to circumvent this limitation, we decided to assay both conditions (condition 1 [+/− saliva] and condition 2 [saliva +/− lysine]). We reasoned that if aggregation elicited a specific and reproducible genetic response, then both microarray approaches should yield a shared geneset, since aggregation would occur in both approaches (Fig.
5
). In contrast, responsive genes that were uniquely affected in either experiment were most likely attributable to factors other than aggregation, such as differences in medium composition (Fig.
4
). Thus, we compared the results from the two microarray experiments using a cutoff value of a ≥2-fold change and were able to identify 96 genes with similar responses in both experiments. In microarray condition 1, this left 51 gene responses that were unlike those of microarray condition 2 and 73 responses in condition 2 that were dissimilar to microarray condition 1 (Fig.
4
). From these results, we concluded that the 96 genes similarly affected in both experiments were most likely to be specifically responsive to aggregation, whereas the 51 and 73 unique gene responses in microarray conditions 1 and 2 were probably due to other factors. Consequently, we focused our analysis on the shared geneset of 96 genes. From this group of genes, 54 exhibited changes of ≥3-fold. Interestingly, the vast majority of the 96 aggregation-specific genes were upregulated, while only four genes were downregulated. This result is consistent with the activation of a genetic program. Although we did not detect changes in the transcription of any putative sigma factors, we did identify three putative transcription regulators that were induced, FN0795, FN1439, and FN1803, which may account for some of the observed gene expression changes in the list (Table
2
). FN1803 was particularly interesting because its microarray signal intensity in the dispersed samples was quite weak, much lower than FN0795 and approximately half that of FN1439 (see Table S1 in the supplemental material). FN1803 also exhibited the greatest increase in expression due to aggregation (three- to fivefold).

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